Hippocampal phenomena that have been theorized to support biological spatial
cognition include (1) self-stabilizing activity patterns in attractor map
networks and (2) temporal-phase organization relative to global oscillations. On
the basis of these phenomena, we introduce a brain-inspired dynamical controller
for self-organized swarms of autonomous agents. Our key realization was that
each virtual or robotic agent can be represented as a spatial neuron (e.g.,
a place cell) whose place preference follows the location of the agent in
its local environment. The analogy of a multi-agent group (or swarm) to a
space-coding neural network follows immediately. If we further suppose that
inter-agent distances map to `synaptic weights' and, consequently, that relative
agent movements map to changes in those weights, then spatial configurations of
the swarm constitute an attractor map network [1]}, [2]}, [3]}
and the swarm's internal motion dynamics constitute learning based on synaptic
modification [4]}, [5]}.
